搜索资源列表
NSGA-II
- MATLAB的NSGA改进后的算法-MATLAB' s NSGA improved algorithm
NSGA-II
- nsga-2算法实现多目标优化,改进了nsga算法。是一种先进的遗传算法-nsga-2 multi-objective optimization algorithm, improved nsga algorithm. Is an advanced genetic algorithm
NSGA-II
- 针对现有改进和声搜索算法(IHS) 的不足，提出一种自适应和声粒子群搜索算法(AHSPSO).-For the purpose of avoiding the disadvantage of improved harmony search (IHS) algorithm, an adaptive harmony search-particle swarm optimization (AHSPSO) algorithm is presented.
nsga2-gnuplot-v1.1.6
- improved NSGA 2 based optimization code
nsga2-gnuplot-v1.1
- improved NSGA ii code
MOEA-NSGA-II
- NSGA算法是遗传算法在多目标优化上的实现，NSGA-2则是NSGA算法的改进算法，该程序包能实现NSGA-2的多目标优化-NSGA algorithm is a genetic algorithm on a multi-objective optimization, NSGA-2 is the improved algorithm NSGA algorithm, the package can achieve multi-objective optimization NSGA-2
Improved-NSGA-II-Optimization
- 对NSGA-II算法进行改进，并应用于柔性车间调度的多目标优化问题-Of NSGA-II algorithm was improved and applied to flexible shop scheduling multi-objective optimization problem
遗传优化工具箱 - NSGA-II
- 改进的遗传算法，多目标优化算法，简单，快捷有效(Improved Genetic Algorithm)
A-NSGA-III
- 新版改进NSGA算法A-NSGA-III，用于软件仿真平台对比于MOEA/D,PSO等多种算法。(The new version of improved NSGA algorithm a-nsga-iii is used for software simulation platform comparison with MOEA/D,PSO and other algorithms.)
NSGA
- 多目标遗传算法是NSGA-II[1]（改进的非支配排序算法）,该遗传算法相比于其它的多目标遗传算法有如下优点：传统的非支配排序算法的复杂度为 ，而NSGA-II的复杂度为 ，其中M为目标函数的个数，N为种群中的个体数。引进精英策略，保证某些优良的种群个体在进化过程中不会被丢弃，从而提高了优化结果的精度。采用拥挤度和拥挤度比较算子，不但克服了NSGA中需要人为指定共享参数的缺陷，而且将其作为种群中个体间的比较标准，使得准Pareto域中的个体能均匀地扩展到整个Pareto域，保证了种群的多样性